---
[](https://pypi.python.org/pypi/lazyqml)
LazyQML is a Python library designed to streamline, automate, and accelerate experimentation with Quantum Machine Learning (QML) architectures, right on classical computers.
With LazyQML, you can:
- đ ïž Build, test, and benchmark QML models with minimal effort.
- ⥠Compare different QML architectures, hyperparameters seamlessly.
- đ§ Gather knowledge about the most suitable architecture for your problem.
## âš Why LazyQML?
- Rapid Prototyping: Experiment with different QML models using just a few lines of code.
- Automated Benchmarking: Evaluate performance and trade-offs across architectures effortlessly.
- Flexible & Modular: From basic quantum circuits to hybrid quantum-classical modelsâLazyQML has you covered.
## Documentation
For detailed usage instructions, API reference, and code examples, please refer to the official LazyQML [documentation](https://qhpc-sp-research-lab.github.io/LazyQML/).
## Requirements
- Python >= 3.10
> ââ
> This library is only supported by Linux Systems. It doesn't support Windows nor MacOS.
> Only supports CUDA compatible devices.
## Installation
To install lazyqml, run this command in your terminal:
```
pip install lazyqml
```
This is the preferred method to install lazyqml, as it will always install the most recent stable release.
If you don't have [pip](https://pip.pypa.io) installed, this [Python installation guide](http://docs.python-guide.org/en/latest/starting/installation/) can guide you through the process.
### From sources
To install lazyqml from sources, run this command in your terminal:
```
pip install git+https://github.com/QHPC-SP-Research-Lab/LazyQML
```
## Example
```python
from sklearn.datasets import load_iris
from lazyqml import *
# Load data
data = load_iris()
X = data.data
y = data.target
classifier = QuantumClassifier(nqubits={4}, classifiers={Model.QNN, Model.QSVM}, epochs=10)
# Fit and predict
classifier.fit(X=X, y=y, test_size=0.4)
```
## Quantum and High Performance Computing (QHPC) - University of Oviedo
- José Ranilla Pastor - ranilla@uniovi.es
- ElĂas FernĂĄndez Combarro - efernandezca@uniovi.es
- Diego GarcĂa Vega - diegogarciavega@gmail.com
- Fernando Ălvaro Plou Llorente - ploufernando@uniovi.es
- Alejandro Leal Castaño - lealcalejandro@uniovi.es
- Group - https://qhpc.uniovi.es
## Citing
If you used LazyQML in your work, please cite:
- GarcĂa-Vega, D., Plou Llorente, F., Leal Castaño, A., Combarro, E.F., Ranilla, J.: Lazyqml: A python library to benchmark quantum machine learning models. In: 30th European Conference on Parallel and Distributed Processing (2024)
## License
- Free software: MIT License
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